Predicting progression in subjective cognitive decline (SCD) using a machine learning (ML) approach: The role of the complaint’s severity

  1. Felpete, Alba 1
  2. Valladares‐Rodríguez, Sonia 2
  3. Mallo, Sabela C. 1
  4. Lojo‐Seoane, Cristina 1
  5. Facal, David 1
  6. Belleville, Sylvie 3
  7. Juncos‐Rabadán, Onésimo 1
  8. Pereiro, Arturo X. 1
  1. 1 University of Santiago de Compostela Santiago de Compostela Spain
  2. 2 University of Vigo Vigo Spain
  3. 3 Université de Montréal Montréal QC Canada
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Journal:
Alzheimer's & Dementia

ISSN: 1552-5260,  1552-5279

Year of publication: 2020

Volume: 16

Issue: S6

Type: Article

DOI: 10.1002/ALZ.043492 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Alzheimer's & Dementia

Abstract

Presence of significant subjective complaints about cognition (SCD) is considered the first behavioral manifestation of Alzheimer disease (AD). However, SCD has not yet overcome the challenge of becoming a reliable preclinical AD marker. Severity indices were proposed to improve the accuracy of complaints when predicting the risk of AD (Jessen et al., 2010).Our aim was to compare the predictive accuracy of ML algorithms using a more (95%ile) or less (5%ile) restrictive cut-off point in severity of complaints for classification in Low (LSC) and High (HSC) subjective complaints groups.